{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Federated Learning: Train FL Model in Python\n",
    "\n",
    "In the \"[Part 01 - Create Plan](Part%2001%20-%20Create%20Plan.ipynb)\" notebook we created the model, training plan, and averaging plan, and then hosted all of them in PyGrid.\n",
    "\n",
    "Such hosted FL model can be now trained using client libraries, SwiftSyft, KotlinSyft, syft.js.\n",
    "\n",
    "In this notebook, we'll use FL Client included in the PySyft to do the training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up Sandbox...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import torch as th\n",
    "from torchvision import datasets, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import urllib3\n",
    "import time\n",
    "\n",
    "import syft as sy\n",
    "from syft.federated.fl_client import FLClient\n",
    "from syft.federated.fl_job import FLJob\n",
    "from syft.grid.clients.model_centric_fl_client import ModelCentricFLClient\n",
    "\n",
    "urllib3.disable_warnings()\n",
    "sy.make_hook(globals())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "private_key = \"\"\"\n",
    "-----BEGIN RSA PRIVATE KEY-----\n",
    "MIIEowIBAAKCAQEAzQMcI09qonB9OZT20X3Z/oigSmybR2xfBQ1YJ1oSjQ3YgV+G\n",
    "FUuhEsGDgqt0rok9BreT4toHqniFixddncTHg7EJzU79KZelk2m9I2sEsKUqEsEF\n",
    "lMpkk9qkPHhJB5AQoClOijee7UNOF4yu3HYvGFphwwh4TNJXxkCg69/RsvPBIPi2\n",
    "9vXFQzFE7cbN6jSxiCtVrpt/w06jJUsEYgNVQhUFABDyWN4h/67M1eArGA540vyd\n",
    "kYdSIEQdknKHjPW62n4dvqDWxtnK0HyChsB+LzmjEnjTJqUzr7kM9Rzq3BY01DNi\n",
    "TVcB2G8t/jICL+TegMGU08ANMKiDfSMGtpz3ZQIDAQABAoIBAD+xbKeHv+BxxGYE\n",
    "Yt5ZFEYhGnOk5GU/RRIjwDSRplvOZmpjTBwHoCZcmsgZDqo/FwekNzzuch1DTnIV\n",
    "M0+V2EqQ0TPJC5xFcfqnikybrhxXZAfpkhtU+gR5lDb5Q+8mkhPAYZdNioG6PGPS\n",
    "oGz8BsuxINhgJEfxvbVpVNWTdun6hLOAMZaH3DHgi0uyTBg8ofARoZP5RIbHwW+D\n",
    "p+5vd9x/x7tByu76nd2UbMp3yqomlB5jQktqyilexCIknEnfb3i/9jqFv8qVE5P6\n",
    "e3jdYoJY+FoomWhqEvtfPpmUFTY5lx4EERCb1qhWG3a7sVBqTwO6jJJBsxy3RLIS\n",
    "Ic0qZcECgYEA6GsBP11a2T4InZ7cixd5qwSeznOFCzfDVvVNI8KUw+n4DOPndpao\n",
    "TUskWOpoV8MyiEGdQHgmTOgGaCXN7bC0ERembK0J64FI3TdKKg0v5nKa7xHb7Qcv\n",
    "t9ccrDZVn4y/Yk5PCqjNWTR3/wDR88XouzIGaWkGlili5IJqdLEvPvUCgYEA4dA+\n",
    "5MNEQmNFezyWs//FS6G3lTRWgjlWg2E6BXXvkEag6G5SBD31v3q9JIjs+sYdOmwj\n",
    "kfkQrxEtbs173xgYWzcDG1FI796LTlJ/YzuoKZml8vEF3T8C4Bkbl6qj9DZljb2j\n",
    "ehjTv5jA256sSUEqOa/mtNFUbFlBjgOZh3TCsLECgYAc701tdRLdXuK1tNRiIJ8O\n",
    "Enou26Thm6SfC9T5sbzRkyxFdo4XbnQvgz5YL36kBnIhEoIgR5UFGBHMH4C+qbQR\n",
    "OK+IchZ9ElBe8gYyrAedmgD96GxH2xAuxAIW0oDgZyZgd71RZ2iBRY322kRJJAdw\n",
    "Xq77qo6eXTKpni7grjpijQKBgDHWRAs5DVeZkTwhoyEW0fRfPKUxZ+ZVwUI9sxCB\n",
    "dt3guKKTtoY5JoOcEyJ9FdBC6TB7rV4KGiSJJf3OXAhgyP9YpNbimbZW52fhzTuZ\n",
    "bwO/ZWC40RKDVZ8f63cNsiGz37XopKvNzu36SJYv7tY8C5WvvLsrd/ZxvIYbRUcf\n",
    "/dgBAoGBAMdR5DXBcOWk3+KyEHXw2qwWcGXyzxtca5SRNLPR2uXvrBYXbhFB/PVj\n",
    "h3rGBsiZbnIvSnSIE+8fFe6MshTl2Qxzw+F2WV3OhhZLLtBnN5qqeSe9PdHLHm49\n",
    "XDce6NV2D1mQLBe8648OI5CScQENuRGxF2/h9igeR4oRRsM1gzJN\n",
    "-----END RSA PRIVATE KEY-----\n",
    "\"\"\".strip()\n",
    "\n",
    "public_key = \"\"\"\n",
    "-----BEGIN PUBLIC KEY-----\n",
    "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAzQMcI09qonB9OZT20X3Z\n",
    "/oigSmybR2xfBQ1YJ1oSjQ3YgV+GFUuhEsGDgqt0rok9BreT4toHqniFixddncTH\n",
    "g7EJzU79KZelk2m9I2sEsKUqEsEFlMpkk9qkPHhJB5AQoClOijee7UNOF4yu3HYv\n",
    "GFphwwh4TNJXxkCg69/RsvPBIPi29vXFQzFE7cbN6jSxiCtVrpt/w06jJUsEYgNV\n",
    "QhUFABDyWN4h/67M1eArGA540vydkYdSIEQdknKHjPW62n4dvqDWxtnK0HyChsB+\n",
    "LzmjEnjTJqUzr7kM9Rzq3BY01DNiTVcB2G8t/jICL+TegMGU08ANMKiDfSMGtpz3\n",
    "ZQIDAQAB\n",
    "-----END PUBLIC KEY-----\n",
    "\"\"\".strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating authentication token."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.e30.Cn_0cSjCw1QKtcYDx_mYN_q9jO2KkpcUoiVbILmKVB4LUCQvZ7YeuyQ51r9h3562KQoSas_ehbjpz2dw1Dk24hQEoN6ObGxfJDOlemF5flvLO_sqAHJDGGE24JRE4lIAXRK6aGyy4f4kmlICL6wG8sGSpSrkZlrFLOVRJckTptgaiOTIm5Udfmi45NljPBQKVpqXFSmmb3dRy_e8g3l5eBVFLgrBhKPQ1VbNfRK712KlQWs7jJ31fGpW2NxMloO1qcd6rux48quivzQBCvyK8PV5Sqrfw_OMOoNLcSvzePDcZXa2nPHSu3qQIikUdZIeCnkJX-w0t8uEFG3DfH1fVA\n"
     ]
    }
   ],
   "source": [
    "import jwt\n",
    "auth_token = jwt.encode({}, private_key, algorithm='RS256').decode('ascii')\n",
    "\n",
    "print(auth_token)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Define `on_accepted`, `on_rejected`, `on_error` handlers.\n",
    "\n",
    "The main training loop is located inside `on_accepted` routine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "cycles_log = []\n",
    "status = {\n",
    "    \"ended\": False\n",
    "}\n",
    "\n",
    "# Called when client is accepted into FL cycle\n",
    "def on_accepted(job: FLJob):\n",
    "    print(f\"Accepted into cycle {len(cycles_log) + 1}!\")\n",
    "\n",
    "    cycle_params = job.client_config\n",
    "    batch_size = cycle_params[\"batch_size\"]\n",
    "    lr = cycle_params[\"lr\"]\n",
    "    max_updates = cycle_params[\"max_updates\"]\n",
    "\n",
    "    mnist_dataset = th.utils.data.DataLoader(\n",
    "        datasets.MNIST('data', train=True, download=True, transform=transforms.ToTensor()),\n",
    "        batch_size=batch_size,\n",
    "        drop_last=True,\n",
    "        shuffle=True,\n",
    "    )\n",
    "\n",
    "    training_plan = job.plans[\"training_plan\"]\n",
    "    model_params = job.model.tensors()\n",
    "    losses = []\n",
    "    accuracies = []\n",
    "\n",
    "    for batch_idx, (X, y) in enumerate(mnist_dataset):\n",
    "        X = X.view(batch_size, -1)\n",
    "        y_oh = th.nn.functional.one_hot(y, 10)\n",
    "        loss, acc, *model_params = training_plan.torchscript(\n",
    "            X, y_oh, th.tensor(batch_size), th.tensor(lr), model_params\n",
    "        )\n",
    "        losses.append(loss.item())\n",
    "        accuracies.append(acc.item())\n",
    "        if batch_idx % 50 == 0:\n",
    "            print(\"Batch %d, loss: %f, accuracy: %f\" % (batch_idx, loss, acc))\n",
    "        if batch_idx >= max_updates:\n",
    "            break\n",
    "\n",
    "    job.report(model_params)\n",
    "    # Save losses/accuracies from cycle\n",
    "    cycles_log.append((losses, accuracies))\n",
    "\n",
    "# Called when the client is rejected from cycle\n",
    "def on_rejected(job: FLJob, timeout):\n",
    "    if timeout is None:\n",
    "        print(f\"Rejected from cycle without timeout (this means FL training is done)\")\n",
    "    else:\n",
    "        print(f\"Rejected from cycle with timeout: {timeout}\")\n",
    "    status[\"ended\"] = True\n",
    "\n",
    "# Called when error occured\n",
    "def on_error(job: FLJob, error: Exception):\n",
    "    print(f\"Error: {error}\")\n",
    "    status[\"ended\"] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We use same PyGrid Node where the model was hosted, the model name/version of hosted model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# PyGrid Node address\n",
    "gridAddress = \"ws://127.0.0.1:5000\"\n",
    "\n",
    "# Hosted model name/version\n",
    "model_name = \"mnist\"\n",
    "model_version = \"1.0.0\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define routine that creates FL client and starts the FL process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_client_and_run_cycle():\n",
    "    client = FLClient(url=gridAddress, auth_token=auth_token, verbose=True)\n",
    "    job = client.new_job(model_name, model_version)\n",
    "\n",
    "    # Set event handlers\n",
    "    job.add_listener(job.EVENT_ACCEPTED, on_accepted)\n",
    "    job.add_listener(job.EVENT_REJECTED, on_rejected)\n",
    "    job.add_listener(job.EVENT_ERROR, on_error)\n",
    "\n",
    "    # Shoot!\n",
    "    job.start()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now we're ready to start FL training.\n",
    "\n",
    "We're going to run multiple \"workers\" until the FL model is fully done and see the progress.\n",
    "\n",
    "As we create & authenticate new client each time,\n",
    "this emulates multiple different workers requesting a cycle and working on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accepted into cycle 1!\n",
      "Batch 0, loss: 2.294856, accuracy: 0.125000\n",
      "Batch 50, loss: 2.237499, accuracy: 0.312500\n",
      "Batch 100, loss: 2.194899, accuracy: 0.515625\n",
      "Accepted into cycle 2!\n",
      "Batch 0, loss: 2.177479, accuracy: 0.609375\n",
      "Batch 50, loss: 2.121224, accuracy: 0.625000\n",
      "Batch 100, loss: 2.102282, accuracy: 0.687500\n",
      "Accepted into cycle 3!\n",
      "Batch 0, loss: 2.063166, accuracy: 0.671875\n",
      "Batch 50, loss: 2.036484, accuracy: 0.609375\n",
      "Batch 100, loss: 1.923608, accuracy: 0.703125\n",
      "Accepted into cycle 4!\n",
      "Batch 0, loss: 1.978470, accuracy: 0.656250\n",
      "Batch 50, loss: 1.796764, accuracy: 0.750000\n",
      "Batch 100, loss: 1.740797, accuracy: 0.765625\n",
      "Accepted into cycle 5!\n",
      "Batch 0, loss: 1.791913, accuracy: 0.687500\n",
      "Batch 50, loss: 1.699130, accuracy: 0.812500\n",
      "Batch 100, loss: 1.609210, accuracy: 0.656250\n",
      "Rejected from cycle without timeout (this means FL training is done)\n"
     ]
    }
   ],
   "source": [
    "while not status[\"ended\"]:\n",
    "    create_client_and_run_cycle()\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let's plot loss and accuracy statistics recorded from each worker.\n",
    "Each such worker's statistics is drawn with different color.\n",
    "\n",
    "It's visible that loss/accuracy improvement occurs after each `max_diffs` reports to PyGrid,\n",
    "because PyGrid updates the model and creates new checkpoint after\n",
    "receiving `max_diffs` updates from FL clients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cycle 1:\tLoss: 2.2441322543833517\tAcc: 0.31079826732673266\n",
      "Cycle 2:\tLoss: 2.1361456861590393\tAcc: 0.5880259900990099\n",
      "Cycle 3:\tLoss: 2.008516544162637\tAcc: 0.6882735148514851\n",
      "Cycle 4:\tLoss: 1.8490276690756922\tAcc: 0.7120977722772277\n",
      "Cycle 5:\tLoss: 1.6852542303576328\tAcc: 0.7263304455445545\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, figsize=(10, 10))\n",
    "axs[0].set_title(\"Loss\")\n",
    "axs[1].set_title(\"Accuracy\")\n",
    "offset = 0\n",
    "for i, cycle_log in enumerate(cycles_log):\n",
    "    losses, accuracies = cycle_log\n",
    "    x = range(offset, offset + len(losses))\n",
    "    axs[0].plot(x, losses)\n",
    "    axs[1].plot(x, accuracies)\n",
    "    offset += len(losses)\n",
    "    print(f\"Cycle {i + 1}:\\tLoss: {np.mean(losses)}\\tAcc: {np.mean(accuracies)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
